Overview

Dataset statistics

Number of variables17
Number of observations33908
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 MiB
Average record size in memory136.0 B

Variable types

Numeric9
Categorical8

Alerts

feature_5 is highly correlated with feature_15High correlation
feature_6 is highly correlated with feature_5 and 1 other fieldsHigh correlation
feature_14 is highly correlated with feature_2 and 2 other fieldsHigh correlation
feature_13 is highly correlated with feature_14High correlation
feature_15 is highly correlated with feature_5High correlation
feature_0 is highly correlated with feature_7High correlation
feature_2 is highly correlated with feature_14High correlation
feature_7 is highly correlated with feature_0 and 1 other fieldsHigh correlation
feature_9 is highly correlated with feature_7High correlation
feature_11 is highly correlated with feature_14High correlation
feature_7 has 3891 (11.5%) zeros Zeros
feature_14 has 2193 (6.5%) zeros Zeros

Reproduction

Analysis started2022-11-12 04:11:07.994261
Analysis finished2022-11-12 04:11:20.726886
Duration12.73 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

feature_0
Real number (ℝ)

HIGH CORRELATION

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.004157719312
Minimum-2.159994149
Maximum5.091402133
Zeros0
Zeros (%)0.0%
Negative18608
Negative (%)54.9%
Memory size265.0 KiB
2022-11-11T22:11:20.809460image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-2.159994149
5-th percentile-1.31242835
Q1-0.7473844836
median-0.1823406175
Q30.6652251816
95-th percentile1.701138936
Maximum5.091402133
Range7.251396281
Interquartile range (IQR)1.412609665

Descriptive statistics

Standard deviation0.9997758651
Coefficient of variation (CV)-240.4625686
Kurtosis0.3192048614
Mean-0.004157719312
Median Absolute Deviation (MAD)0.6592178438
Skewness0.6862668522
Sum-140.9799464
Variance0.9995517804
MonotonicityNot monotonic
2022-11-11T22:11:20.911600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.84155846131567
 
4.6%
-0.9357324391486
 
4.4%
-0.74738448361480
 
4.4%
-0.55903652821443
 
4.3%
-0.65321050591428
 
4.2%
-0.46486255051370
 
4.0%
-1.0299064171327
 
3.9%
-0.37068857291286
 
3.8%
-0.27651459521103
 
3.3%
-0.18234061751093
 
3.2%
Other values (67)20325
59.9%
ValueCountFrequency (%)
-2.15999414910
 
< 0.1%
-2.06582017122
 
0.1%
-1.97164619334
 
0.1%
-1.87747221663
 
0.2%
-1.783298238104
 
0.3%
-1.68912426157
 
0.5%
-1.594950283231
 
0.7%
-1.500776305404
1.2%
-1.406602327609
1.8%
-1.31242835699
2.1%
ValueCountFrequency (%)
5.0914021331
 
< 0.1%
4.9972281551
 
< 0.1%
4.9030541771
 
< 0.1%
4.80888022
 
< 0.1%
4.6205322441
 
< 0.1%
4.5263582673
< 0.1%
4.4321842892
 
< 0.1%
4.3380103113
< 0.1%
4.2438363336
< 0.1%
4.1496623562
 
< 0.1%

feature_1
Real number (ℝ)

Distinct6434
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00258382466
Minimum-3.081148547
Maximum33.09477576
Zeros0
Zeros (%)0.0%
Negative25074
Negative (%)73.9%
Memory size265.0 KiB
2022-11-11T22:11:21.009876image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-3.081148547
5-th percentile-0.5039103537
Q1-0.4227866333
median-0.2973240375
Q30.02290117447
95-th percentile1.448855832
Maximum33.09477576
Range36.1759243
Interquartile range (IQR)0.4456878078

Descriptive statistics

Standard deviation1.014267778
Coefficient of variation (CV)392.5451263
Kurtosis149.2496233
Mean0.00258382466
Median Absolute Deviation (MAD)0.1500953045
Skewness8.638637558
Sum87.61232658
Variance1.028739124
MonotonicityNot monotonic
2022-11-11T22:11:21.100876image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.44741934192611
 
7.7%
-0.4470909058134
 
0.4%
-0.4467624697125
 
0.4%
-0.4461055975108
 
0.3%
-0.4464340336107
 
0.3%
-0.445777161484
 
0.2%
-0.445448725368
 
0.2%
-0.439865311360
 
0.2%
-0.44479185359
 
0.2%
-0.444134980851
 
0.2%
Other values (6424)30501
90.0%
ValueCountFrequency (%)
-3.0811485471
< 0.1%
-2.696221421
< 0.1%
-1.779884661
< 0.1%
-1.5549059211
< 0.1%
-1.4517769811
< 0.1%
-1.3759082391
< 0.1%
-1.3026669851
< 0.1%
-1.1969105561
< 0.1%
-1.1443607781
< 0.1%
-1.134836131
< 0.1%
ValueCountFrequency (%)
33.094775761
< 0.1%
31.876277771
< 0.1%
26.222906931
< 0.1%
22.93329081
< 0.1%
21.466166671
< 0.1%
21.443833021
< 0.1%
20.685145591
< 0.1%
19.143466471
< 0.1%
18.780544561
< 0.1%
18.416308911
< 0.1%

feature_2
Real number (ℝ)

HIGH CORRELATION

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0002127901277
Minimum-1.779107836
Maximum1.82562845
Zeros0
Zeros (%)0.0%
Negative16447
Negative (%)48.5%
Memory size265.0 KiB
2022-11-11T22:11:21.340878image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1.779107836
5-th percentile-1.538792084
Q1-0.9380027029
median0.02326030684
Q30.6240496879
95-th percentile1.585312698
Maximum1.82562845
Range3.604736287
Interquartile range (IQR)1.562052391

Descriptive statistics

Standard deviation1.000872259
Coefficient of variation (CV)-4703.56529
Kurtosis-1.061004934
Mean-0.0002127901277
Median Absolute Deviation (MAD)0.8411051335
Skewness0.09063085798
Sum-7.21528765
Variance1.001745278
MonotonicityNot monotonic
2022-11-11T22:11:21.421877image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.50389181172090
 
6.2%
0.26357605931752
 
5.2%
0.62404968791488
 
4.4%
-1.1783184551449
 
4.3%
0.14341818311438
 
4.2%
-1.2984763321415
 
4.2%
-0.21705544561409
 
4.2%
-0.93800270291393
 
4.1%
1.4651548211369
 
4.0%
-1.0581605791342
 
4.0%
Other values (21)18763
55.3%
ValueCountFrequency (%)
-1.779107836248
 
0.7%
-1.65894996984
2.9%
-1.538792084816
2.4%
-1.4186342081097
3.2%
-1.2984763321415
4.2%
-1.1783184551449
4.3%
-1.0581605791342
4.0%
-0.93800270291393
4.1%
-0.81784482671159
3.4%
-0.6976869505398
 
1.2%
ValueCountFrequency (%)
1.82562845489
 
1.4%
1.7054705741154
3.4%
1.5853126981309
3.9%
1.4651548211369
4.0%
1.344996945874
2.6%
1.224839069776
2.3%
1.104681193628
1.9%
0.9845233166349
 
1.0%
0.8643654404688
2.0%
0.7442075642680
2.0%

feature_3
Real number (ℝ)

Distinct1482
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.287459413 × 10-5
Minimum-1.002477879
Maximum18.09469981
Zeros0
Zeros (%)0.0%
Negative22654
Negative (%)66.8%
Memory size265.0 KiB
2022-11-11T22:11:21.516878image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1.002477879
5-th percentile-0.8665687247
Q1-0.6025166542
median-0.3035165155
Q30.2362369816
95-th percentile1.909861134
Maximum18.09469981
Range19.09717769
Interquartile range (IQR)0.8387536357

Descriptive statistics

Standard deviation1.002511745
Coefficient of variation (CV)-18960.17854
Kurtosis19.42937687
Mean-5.287459413 × 10-5
Median Absolute Deviation (MAD)0.3611300376
Skewness3.214662874
Sum-1.792871738
Variance1.005029799
MonotonicityNot monotonic
2022-11-11T22:11:21.606877image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.652997197153
 
0.5%
-0.5209711618146
 
0.4%
-0.5287373992140
 
0.4%
-0.5753348234133
 
0.4%
-0.6219322476133
 
0.4%
-0.5986335355132
 
0.4%
-0.5326205178131
 
0.4%
-0.4743737376130
 
0.4%
-0.5598023486130
 
0.4%
-0.7190102147129
 
0.4%
Other values (1472)32551
96.0%
ValueCountFrequency (%)
-1.0024778793
 
< 0.1%
-0.99859475991
 
< 0.1%
-0.99471164123
 
< 0.1%
-0.99082852253
 
< 0.1%
-0.986945403910
 
< 0.1%
-0.983062285227
0.1%
-0.979179166538
0.1%
-0.975296047855
0.2%
-0.971412929160
0.2%
-0.967529810455
0.2%
ValueCountFrequency (%)
18.094699811
< 0.1%
14.067905731
< 0.1%
13.695126341
< 0.1%
12.285554261
< 0.1%
11.897242391
< 0.1%
11.749683881
< 0.1%
11.62930721
< 0.1%
11.357488891
< 0.1%
11.011891331
< 0.1%
10.949761431
< 0.1%

feature_4
Real number (ℝ)

Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0002980492524
Minimum-0.5693506376
Maximum19.44364734
Zeros0
Zeros (%)0.0%
Negative22557
Negative (%)66.5%
Memory size265.0 KiB
2022-11-11T22:11:21.703878image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-0.5693506376
5-th percentile-0.5693506376
Q1-0.5693506376
median-0.2465603476
Q30.07622994242
95-th percentile1.690181393
Maximum19.44364734
Range20.01299798
Interquartile range (IQR)0.6455805801

Descriptive statistics

Standard deviation1.003723509
Coefficient of variation (CV)-3367.643104
Kurtosis41.91052474
Mean-0.0002980492524
Median Absolute Deviation (MAD)0.32279029
Skewness5.006205658
Sum-10.10625405
Variance1.007460883
MonotonicityNot monotonic
2022-11-11T22:11:21.793876image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
-0.569350637613160
38.8%
-0.24656034769397
27.7%
0.076229942424153
 
12.2%
0.39902023252640
 
7.8%
0.72181052251310
 
3.9%
1.044600813947
 
2.8%
1.367391103541
 
1.6%
1.690181393401
 
1.2%
2.012971683246
 
0.7%
2.335761973210
 
0.6%
Other values (37)903
 
2.7%
ValueCountFrequency (%)
-0.569350637613160
38.8%
-0.24656034769397
27.7%
0.076229942424153
 
12.2%
0.39902023252640
 
7.8%
0.72181052251310
 
3.9%
1.044600813947
 
2.8%
1.367391103541
 
1.6%
1.690181393401
 
1.2%
2.012971683246
 
0.7%
2.335761973210
 
0.6%
ValueCountFrequency (%)
19.443647341
 
< 0.1%
17.829695891
 
< 0.1%
16.861325021
 
< 0.1%
15.570163861
 
< 0.1%
15.247373572
< 0.1%
13.956212411
 
< 0.1%
13.310631831
 
< 0.1%
12.987841543
< 0.1%
12.342260961
 
< 0.1%
11.373890093
< 0.1%

feature_5
Real number (ℝ)

HIGH CORRELATION

Distinct517
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.004651945576
Minimum-0.4114531065
Maximum8.127647702
Zeros0
Zeros (%)0.0%
Negative27923
Negative (%)82.3%
Memory size265.0 KiB
2022-11-11T22:11:21.891878image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-0.4114531065
5-th percentile-0.4114531065
Q1-0.4114531065
median-0.4114531065
Q3-0.4114531065
95-th percentile2.734531402
Maximum8.127647702
Range8.539100808
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.993983554
Coefficient of variation (CV)-213.6705036
Kurtosis7.035493714
Mean-0.004651945576
Median Absolute Deviation (MAD)0
Skewness2.635938561
Sum-157.7381706
Variance0.9880033057
MonotonicityNot monotonic
2022-11-11T22:11:21.978878image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.411453106527752
81.8%
1.416214084124
 
0.4%
0.5173613674110
 
0.3%
1.426201336101
 
0.3%
0.50737411595
 
0.3%
1.40622683290
 
0.3%
3.29381753773
 
0.2%
1.43618858964
 
0.2%
0.537335872260
 
0.2%
3.23389402259
 
0.2%
Other values (507)5380
 
15.9%
ValueCountFrequency (%)
-0.411453106527752
81.8%
-0.391478601713
 
< 0.1%
-0.381491349325
 
0.1%
-0.37150409691
 
< 0.1%
-0.36151684451
 
< 0.1%
-0.3515295929
 
< 0.1%
-0.34154233968
 
< 0.1%
-0.33155508725
 
< 0.1%
-0.321567834820
 
0.1%
-0.31158058249
 
< 0.1%
ValueCountFrequency (%)
8.1276477021
< 0.1%
8.0078006731
< 0.1%
7.8979408961
< 0.1%
7.8679791391
< 0.1%
7.8480046341
< 0.1%
7.6682340911
< 0.1%
7.6382723341
< 0.1%
7.5084380532
< 0.1%
7.49845081
< 0.1%
7.3786037711
< 0.1%

feature_6
Real number (ℝ)

HIGH CORRELATION

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.007497737708
Minimum-0.2519403707
Maximum23.62564416
Zeros0
Zeros (%)0.0%
Negative27752
Negative (%)81.8%
Memory size265.0 KiB
2022-11-11T22:11:22.068651image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-0.2519403707
5-th percentile-0.2519403707
Q1-0.2519403707
median-0.2519403707
Q3-0.2519403707
95-th percentile1.050473331
Maximum23.62564416
Range23.87758453
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8026964203
Coefficient of variation (CV)-107.0584824
Kurtosis93.45909938
Mean-0.007497737708
Median Absolute Deviation (MAD)0
Skewness7.33024153
Sum-254.2332902
Variance0.6443215431
MonotonicityNot monotonic
2022-11-11T22:11:22.151236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
-0.251940370727752
81.8%
0.18219752992093
 
6.2%
0.61633543061557
 
4.6%
1.050473331851
 
2.5%
1.484611232539
 
1.6%
1.918749132341
 
1.0%
2.352887033209
 
0.6%
2.787024934147
 
0.4%
3.22116283495
 
0.3%
3.65530073571
 
0.2%
Other values (26)253
 
0.7%
ValueCountFrequency (%)
-0.251940370727752
81.8%
0.18219752992093
 
6.2%
0.61633543061557
 
4.6%
1.050473331851
 
2.5%
1.484611232539
 
1.6%
1.918749132341
 
1.0%
2.352887033209
 
0.6%
2.787024934147
 
0.4%
3.22116283495
 
0.3%
3.65530073571
 
0.2%
ValueCountFrequency (%)
23.625644161
 
< 0.1%
17.547713551
 
< 0.1%
16.245299851
 
< 0.1%
15.811161952
< 0.1%
13.640472451
 
< 0.1%
12.772196653
< 0.1%
12.338058753
< 0.1%
11.903920852
< 0.1%
11.469782953
< 0.1%
11.035645051
 
< 0.1%

feature_7
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.336380795
Minimum0
Maximum11
Zeros3891
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size265.0 KiB
2022-11-11T22:11:22.223236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q37
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.27337627
Coefficient of variation (CV)0.7548636581
Kurtosis-1.267428369
Mean4.336380795
Median Absolute Deviation (MAD)3
Skewness0.2651690537
Sum147038
Variance10.7149922
MonotonicityNot monotonic
2022-11-11T22:11:22.285235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
17286
21.5%
47137
21.0%
95691
16.8%
03891
11.5%
73094
9.1%
51683
 
5.0%
61164
 
3.4%
21118
 
3.3%
10988
 
2.9%
3938
 
2.8%
Other values (2)918
 
2.7%
ValueCountFrequency (%)
03891
11.5%
17286
21.5%
21118
 
3.3%
3938
 
2.8%
47137
21.0%
51683
 
5.0%
61164
 
3.4%
73094
9.1%
8700
 
2.1%
95691
16.8%
ValueCountFrequency (%)
11218
 
0.6%
10988
 
2.9%
95691
16.8%
8700
 
2.1%
73094
9.1%
61164
 
3.4%
51683
 
5.0%
47137
21.0%
3938
 
2.8%
21118
 
3.3%

feature_8
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.0 KiB
1
20434 
2
9637 
0
3837 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33908
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
120434
60.3%
29637
28.4%
03837
 
11.3%

Length

2022-11-11T22:11:22.355235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T22:11:22.440235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
120434
60.3%
29637
28.4%
03837
 
11.3%

Most occurring characters

ValueCountFrequency (%)
120434
60.3%
29637
28.4%
03837
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
120434
60.3%
29637
28.4%
03837
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common33908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
120434
60.3%
29637
28.4%
03837
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII33908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120434
60.3%
29637
28.4%
03837
 
11.3%

feature_9
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.0 KiB
1
17380 
2
9974 
0
5147 
3
 
1407

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33908
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
117380
51.3%
29974
29.4%
05147
 
15.2%
31407
 
4.1%

Length

2022-11-11T22:11:22.505234image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T22:11:22.579235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
117380
51.3%
29974
29.4%
05147
 
15.2%
31407
 
4.1%

Most occurring characters

ValueCountFrequency (%)
117380
51.3%
29974
29.4%
05147
 
15.2%
31407
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
117380
51.3%
29974
29.4%
05147
 
15.2%
31407
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common33908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
117380
51.3%
29974
29.4%
05147
 
15.2%
31407
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII33908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
117380
51.3%
29974
29.4%
05147
 
15.2%
31407
 
4.1%

feature_10
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.0 KiB
0
33293 
1
 
615

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
033293
98.2%
1615
 
1.8%

Length

2022-11-11T22:11:22.646235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T22:11:22.715237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
033293
98.2%
1615
 
1.8%

Most occurring characters

ValueCountFrequency (%)
033293
98.2%
1615
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
033293
98.2%
1615
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common33908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
033293
98.2%
1615
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII33908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
033293
98.2%
1615
 
1.8%

feature_11
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.0 KiB
1
18836 
0
15072 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
118836
55.6%
015072
44.4%

Length

2022-11-11T22:11:22.775236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T22:11:22.844235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
118836
55.6%
015072
44.4%

Most occurring characters

ValueCountFrequency (%)
118836
55.6%
015072
44.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
118836
55.6%
015072
44.4%

Most occurring scripts

ValueCountFrequency (%)
Common33908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
118836
55.6%
015072
44.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII33908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118836
55.6%
015072
44.4%

feature_12
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.0 KiB
0
28494 
1
5414 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
028494
84.0%
15414
 
16.0%

Length

2022-11-11T22:11:22.905237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T22:11:22.977238image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
028494
84.0%
15414
 
16.0%

Most occurring characters

ValueCountFrequency (%)
028494
84.0%
15414
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028494
84.0%
15414
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common33908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028494
84.0%
15414
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII33908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028494
84.0%
15414
 
16.0%

feature_13
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.0 KiB
0
21978 
2
9751 
1
 
2179

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33908
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
021978
64.8%
29751
28.8%
12179
 
6.4%

Length

2022-11-11T22:11:23.038235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T22:11:23.111237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
021978
64.8%
29751
28.8%
12179
 
6.4%

Most occurring characters

ValueCountFrequency (%)
021978
64.8%
29751
28.8%
12179
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
021978
64.8%
29751
28.8%
12179
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common33908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
021978
64.8%
29751
28.8%
12179
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII33908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
021978
64.8%
29751
28.8%
12179
 
6.4%

feature_14
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.520496638
Minimum0
Maximum11
Zeros2193
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size265.0 KiB
2022-11-11T22:11:23.170235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q38
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.003241212
Coefficient of variation (CV)0.5440164914
Kurtosis-0.9939361211
Mean5.520496638
Median Absolute Deviation (MAD)2
Skewness-0.478875224
Sum187189
Variance9.019457777
MonotonicityNot monotonic
2022-11-11T22:11:23.230239image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
810273
30.3%
55187
15.3%
14672
13.8%
64021
 
11.9%
93005
 
8.9%
02193
 
6.5%
32013
 
5.9%
41071
 
3.2%
10545
 
1.6%
11427
 
1.3%
Other values (2)501
 
1.5%
ValueCountFrequency (%)
02193
 
6.5%
14672
13.8%
2150
 
0.4%
32013
 
5.9%
41071
 
3.2%
55187
15.3%
64021
 
11.9%
7351
 
1.0%
810273
30.3%
93005
 
8.9%
ValueCountFrequency (%)
11427
 
1.3%
10545
 
1.6%
93005
 
8.9%
810273
30.3%
7351
 
1.0%
64021
 
11.9%
55187
15.3%
41071
 
3.2%
32013
 
5.9%
2150
 
0.4%

feature_15
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.0 KiB
3
27756 
0
3661 
1
 
1365
2
 
1126

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33908
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
327756
81.9%
03661
 
10.8%
11365
 
4.0%
21126
 
3.3%

Length

2022-11-11T22:11:23.298237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T22:11:23.376236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
327756
81.9%
03661
 
10.8%
11365
 
4.0%
21126
 
3.3%

Most occurring characters

ValueCountFrequency (%)
327756
81.9%
03661
 
10.8%
11365
 
4.0%
21126
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
327756
81.9%
03661
 
10.8%
11365
 
4.0%
21126
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common33908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
327756
81.9%
03661
 
10.8%
11365
 
4.0%
21126
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII33908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
327756
81.9%
03661
 
10.8%
11365
 
4.0%
21126
 
3.3%

labels
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.0 KiB
0
29941 
1
3967 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029941
88.3%
13967
 
11.7%

Length

2022-11-11T22:11:23.443236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T22:11:23.515237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
029941
88.3%
13967
 
11.7%

Most occurring characters

ValueCountFrequency (%)
029941
88.3%
13967
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029941
88.3%
13967
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Common33908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029941
88.3%
13967
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII33908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029941
88.3%
13967
 
11.7%

Interactions

2022-11-11T22:11:19.567889image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.002751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.820750image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.604751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:15.517751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.343242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:17.165887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.034887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.783887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:19.656887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.107751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.908753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.697753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:15.610751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.436241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:17.249887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.118886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.871888image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:19.743889image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.199752image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.995753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.787751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:15.699751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.526241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:17.332886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.199887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.962890image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:19.834887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.292753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.085752image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.880751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:15.794751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.620241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:17.418887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.287887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:19.053887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:19.922887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.384751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.176753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.972753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:15.894226image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.713244image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:17.503887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.373886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:19.142892image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:20.014888image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.478753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.268751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:15.068751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:15.991108image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.809243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:17.591888image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.460887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:19.235886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:20.095887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.563751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.350751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:15.252751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.077243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.894243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:17.673889image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.538889image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:19.317886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:20.175887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.648752image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.433751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:15.339754image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.162241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.983016image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:17.751887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.619889image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:19.398887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:20.258887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:13.734751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:14.520751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:15.430753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:16.256243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:17.074889image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:17.830887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:18.700889image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-11T22:11:19.482888image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-11-11T22:11:23.586236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T22:11:23.724235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T22:11:23.867237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T22:11:24.009239image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T22:11:24.144236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-11T22:11:24.443236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T22:11:20.387888image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T22:11:20.603887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

feature_0feature_1feature_2feature_3feature_4feature_5feature_6feature_7feature_8feature_9feature_10feature_11feature_12feature_13feature_14feature_15labels
0-0.276515-0.4244291.344997-0.0122830.0762301.0766480.18219830100001021
10.8535730.1509910.503892-0.979179-0.569351-0.411453-0.2519404120100030
20.947747-0.1738321.825628-0.7034780.076230-0.411453-0.2519406120000530
30.853573-0.3814040.984523-0.039464-0.569351-0.411453-0.2519404020100530
41.3244431.590527-1.178318-0.097711-0.246560-0.411453-0.2519400110000830
51.418617-0.4474191.3449970.154691-0.5693510.7071193.22116341100001011
60.288529-0.322285-0.817845-0.645231-0.569351-0.411453-0.2519409110100530
70.006007-0.332138-0.817845-0.322932-0.569351-0.411453-0.2519409110100530
8-1.5007760.023230-0.0968981.016744-0.569351-0.411453-0.2519408220100830
90.853573-0.4221301.344997-0.466608-0.246560-0.411453-0.2519407110000830

Last rows

feature_0feature_1feature_2feature_3feature_4feature_5feature_6feature_7feature_8feature_9feature_10feature_11feature_12feature_13feature_14feature_15labels
33898-0.747384-0.655648-0.457371-0.369530-0.2465603.0940720.1821984020110800
338991.136095-0.4392080.7442081.047809-0.246560-0.411453-0.2519401110000131
33900-0.276515-0.3906001.465155-0.8782181.690181-0.411453-0.2519404120000130
33901-1.218254-0.400125-0.938003-0.474374-0.569351-0.411453-0.2519400210010530
339020.7593990.257405-1.0581614.884330-0.2465603.1240340.6163351200100811
339031.701139-0.248387-0.457371-0.792789-0.5693514.8518290.61633550000021000
339041.512791-0.433625-1.298476-0.823854-0.569351-0.411453-0.25194010120002830
339050.006007-0.3955260.984523-0.555919-0.246560-0.411453-0.2519400110100730
33906-0.0881670.7891430.503892-0.664647-0.246560-0.411453-0.2519401110100930
339070.947747-0.406365-0.938003-0.606400-0.246560-0.411453-0.2519401130102830